Abstract
Machine learning and data analytics are two of the most popular subdisciplines of modern computer science which have a variety of scopes in most of the industries ranging from hospitals to hotels, manufacturing to pharmaceuticals, mining to banking, etc. Additionally, mining and hospitals are two of the most critical industries where applications when deployed security, accuracy, and cost effectiveness are the major concerns, due to the huge involvement of man and machines. In this paper, the problem of finding out the location of man and machines has been focused on in case of an accident during the mining process. The primary scope of the research is to guarantee that the projected position is near to the real place so that the trained model’s performance can be tested. The solution has been implemented by first proposing the MLAELD (Machine Learning Architecture for Excavators’ Location Detection), in which Bluetooth Low Energy (BLE) beacons have been used for tracking the live locations of excavators preceded by collecting the data of the signal strength mapping from multiple beacons at each specific point in a closed area. Second, machine learning techniques are proposed to develop and train multioutput regression models using linear regression, K-nearest neighbor regression, decision tree regression, and random forest regression. These techniques can predict the live locations of the required persons and machines with a high level of precision from the last beacon strengths received.
Highlights
Machine learning and data analytics are two of the most popular subdisciplines of modern computer science which have a variety of scopes in most of the industries ranging from hospitals to hotels, manufacturing to pharmaceuticals, mining to banking, etc
The problem of finding out the location of man and machines has been focused on in case of an accident during the mining process. e primary scope of the research is to guarantee that the projected position is near to the real place so that the trained model’s performance can be tested. e solution has been implemented by first proposing the MLAELD (Machine Learning Architecture for Excavators’ Location Detection), in which Bluetooth Low Energy (BLE) beacons have been used for tracking the live locations of excavators preceded by collecting the data of the signal strength mapping from multiple beacons at each specific point in a closed area
A combination of Bluetooth technology and machine learning has been used for predicting the accurate locations of excavators. e limitation of GPS has been overcome in underground areas by using Bluetooth Low Energy (BLE) beacons for exchanging the signals at the working site
Summary
Bluetooth beacons can be used to determine precise locations of mobile devices using specific applications. A beacon transmits a Bluetooth Low Energy (BLE) signal within a distance of 50 meters (LOS) that can be detected by compatible devices. Bluetooth Low Energy (BLE) beacons are used to work on low power by transmitting a signal that compatible applications can receive and detect. It is a one-way broadcast where beacons transmit the signal with applications to receive them. E triangulation geometrical approach is quick and simple, straightforward, and easy to build for the positioning algorithm It works well in the absence of interference and barriers.
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